Abstract
The ability of proteomics in detecting particular disease in the early stages intrigues researchers, especially analytical researchers, computer scientists and mathematicians. Further, high throughput of proteomics pattern derived from mass spectrometry analysis has embarked new paradigm for biomarker analysis through accessible body fluids such as serum, saliva, and urine. Recently, sophisticated computational techniques that are mimetic natural survival and behaviour of organisms have been widely adopted in problem-solving algorithm. As we put emphasis on feature selection algorithm, the most challenging phase in biomarker analysis is selecting most parsimonious features of voluminous mass spectrometry data. Therefore this study reveals the hybrid artificial bee colony and differential evolution as feature selection techniques exhibits comparable results. These results were compared with other types of bio-inspired algorithms such as ant colony and particle swarm optimisation. The proposed method produced; 1) 100 percent and 98.44 of accuracy of the ovarian cancer dataset; and 2) 100 percent and 94.44 percent for TOX dataset for both training and testing respectively.
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References
Al-Ani Ahmed. Feature subset selection using ant colony optimization (2005)
Armananzas, R., Saeys, Y., Inza, I., Garcia-Torres, M., Bielza, C., Van de Peer, Y., Larranaga, P.: Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8(3), 760–774 (2011)
Baykasoglu, A., Ozbakir, L., Tapkan, P.: Artificial bee colony algorithm and its application to generalized assignment problem. Swarm Intelligence: Focus on Ant and Particle Swarm Optimization, 113–144 (2007)
Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2010)
Mohamed Yusoff, S.A., Abdullah, R., Venkat, I.: Using ABC algorithm with shrinkage estimator to identify biomarkers of ovarian cancer from mass spectrometry analysis. In: Pan, J.-S., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds.) HAIS 2013. LNCS, vol. 8073, pp. 345–355. Springer, Heidelberg (2013)
Mohd Shukran, M.A., Chung, Y.Y., Yeh, W.C., Wahid, N., Ahmad Zaidi, A.M.: Artificial bee colony based data mining algorithms for classification tasks. Modern Applied Science 5(4), 217 (2011)
Price, K.V.: An introduction to differential evolution. In: New Ideas in Optimization, pp. 79–108. McGraw-Hill Ltd., UK (1999)
Ressom, H.W., Varghese, R.S., Drake, S.K., Hortin, G.L., Abdel-Hamid, M., Loffredo, C.A., Goldman, R.: Peak selection from maldi-tof mass spectra using ant colony optimization. Bioinformatics 23(5), 619–626 (2007)
Ressom, H.W., Varghese, R.S., Abdel-Hamid, M., Eissa, S.A.-L., Saha, D., Goldman, L., Petricoin, E.F., Conrads, T.P., Veenstra, T.D., Loffredo, C.A., Goldman, R.: Analysis of mass spectral serum profiles for biomarker selection. Bioinformatics 21(21), 4039–4045 (2005)
Reynoso-Meza, G., Sanchis, J., Blasco, X., Herrero, J.M.: Hybrid de algorithm with adaptive crossover operator for solving real-world numerical optimization problems. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 1551–1556 (2011)
SyarifahAdilah, M., Abdullah, R., Venkat, I.: Abc algorithm as feature selection for biomarker discovery in mass spectrometry analysis. In: 2012 4th Conference on Data Mining and Optimization (DMO), pp. 67–72. IEEE (2012)
Tu, C.-J., Chuang, L.-Y., Chang, J.-Y., Yang, C.-H., et al.: Feature selection using pso-svm. IAENG International Journal of Computer Science 33(1), 111–116 (2007)
Yusoff, S.A.M., Venkat, I., Yusof, U.K., Abdullah, R.: Bio-inspired metaheuristic optimization algorithms for biomarker identification in mass spectrometry analysis. International Journal of Natural Computing Research (IJNCR) 3(2), 64–85 (2012)
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Yusoff, S.A.M., Abdullah, R., Venkat, I. (2014). Adapted Bio-inspired Artificial Bee Colony and Differential Evolution for Feature Selection in Biomarker Discovery Analysis. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_11
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DOI: https://doi.org/10.1007/978-3-319-07692-8_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07691-1
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